Overview

Dataset statistics

Number of variables16
Number of observations891
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory111.5 KiB
Average record size in memory128.1 B

Variable types

Numeric6
Categorical8
Text2

Alerts

FamilySize is highly overall correlated with Fare and 4 other fieldsHigh correlation
Fare is highly overall correlated with FamilySize and 1 other fieldsHigh correlation
Has_Cabin is highly overall correlated with Fare and 1 other fieldsHigh correlation
IsAlone is highly overall correlated with FamilySize and 2 other fieldsHigh correlation
Parch is highly overall correlated with FamilySize and 1 other fieldsHigh correlation
Pclass is highly overall correlated with Has_CabinHigh correlation
Sex is highly overall correlated with Survived and 1 other fieldsHigh correlation
SibSp is highly overall correlated with FamilySize and 2 other fieldsHigh correlation
Survived is highly overall correlated with Sex and 1 other fieldsHigh correlation
Title is highly overall correlated with Sex and 1 other fieldsHigh correlation
ticket_type is highly overall correlated with FamilySize and 1 other fieldsHigh correlation
ticket_type is highly imbalanced (58.4%)Imbalance
PassengerId is uniformly distributedUniform
PassengerId has unique valuesUnique
Name has unique valuesUnique
SibSp has 608 (68.2%) zerosZeros
Parch has 678 (76.1%) zerosZeros
Fare has 15 (1.7%) zerosZeros

Reproduction

Analysis started2026-01-22 16:59:24.460592
Analysis finished2026-01-22 16:59:32.407236
Duration7.95 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-22T16:59:32.536731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.5
Q1223.5
median446
Q3668.5
95-th percentile846.5
Maximum891
Range890
Interquartile range (IQR)445

Descriptive statistics

Standard deviation257.35384
Coefficient of variation (CV)0.57702655
Kurtosis-1.2
Mean446
Median Absolute Deviation (MAD)223
Skewness0
Sum397386
Variance66231
MonotonicityStrictly increasing
2026-01-22T16:59:32.715936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8911
 
0.1%
11
 
0.1%
21
 
0.1%
31
 
0.1%
41
 
0.1%
51
 
0.1%
61
 
0.1%
71
 
0.1%
81
 
0.1%
91
 
0.1%
Other values (881)881
98.9%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
8911
0.1%
8901
0.1%
8891
0.1%
8881
0.1%
8871
0.1%
8861
0.1%
8851
0.1%
8841
0.1%
8831
0.1%
8821
0.1%

Survived
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
0
549 
1
342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Length

2026-01-22T16:59:32.959946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T16:59:33.080223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Most occurring characters

ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0549
61.6%
1342
38.4%

Pclass
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
3
491 
1
216 
2
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Length

2026-01-22T16:59:33.232807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T16:59:33.361263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Most occurring characters

ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3491
55.1%
1216
24.2%
2184
 
20.7%

Name
Text

Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2026-01-22T16:59:33.898440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length82
Median length52
Mean length26.965208
Min length12

Characters and Unicode

Total characters24026
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique891 ?
Unique (%)100.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowCumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd rowHeikkinen, Miss. Laina
4th rowFutrelle, Mrs. Jacques Heath (Lily May Peel)
5th rowAllen, Mr. William Henry
ValueCountFrequency (%)
mr521
 
14.4%
miss182
 
5.0%
mrs129
 
3.6%
william64
 
1.8%
john44
 
1.2%
master40
 
1.1%
henry35
 
1.0%
james24
 
0.7%
george24
 
0.7%
charles23
 
0.6%
Other values (1515)2538
70.0%
2026-01-22T16:59:34.734459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2735
 
11.4%
r1958
 
8.1%
e1703
 
7.1%
a1657
 
6.9%
i1325
 
5.5%
n1304
 
5.4%
s1297
 
5.4%
M1128
 
4.7%
l1067
 
4.4%
o1008
 
4.2%
Other values (50)8844
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)24026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r1958
 
8.1%
e1703
 
7.1%
a1657
 
6.9%
i1325
 
5.5%
n1304
 
5.4%
s1297
 
5.4%
M1128
 
4.7%
l1067
 
4.4%
o1008
 
4.2%
Other values (50)8844
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r1958
 
8.1%
e1703
 
7.1%
a1657
 
6.9%
i1325
 
5.5%
n1304
 
5.4%
s1297
 
5.4%
M1128
 
4.7%
l1067
 
4.4%
o1008
 
4.2%
Other values (50)8844
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r1958
 
8.1%
e1703
 
7.1%
a1657
 
6.9%
i1325
 
5.5%
n1304
 
5.4%
s1297
 
5.4%
M1128
 
4.7%
l1067
 
4.4%
o1008
 
4.2%
Other values (50)8844
36.8%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
male
577 
female
314 

Length

Max length6
Median length4
Mean length4.704826
Min length4

Characters and Unicode

Total characters4192
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male577
64.8%
female314
35.2%

Length

2026-01-22T16:59:35.008060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T16:59:35.146733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male577
64.8%
female314
35.2%

Most occurring characters

ValueCountFrequency (%)
e1205
28.7%
m891
21.3%
a891
21.3%
l891
21.3%
f314
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)4192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1205
28.7%
m891
21.3%
a891
21.3%
l891
21.3%
f314
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1205
28.7%
m891
21.3%
a891
21.3%
l891
21.3%
f314
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1205
28.7%
m891
21.3%
a891
21.3%
l891
21.3%
f314
 
7.5%

Age
Real number (ℝ)

Distinct88
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.361582
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-22T16:59:35.312105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile6
Q122
median28
Q335
95-th percentile54
Maximum80
Range79.58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.019697
Coefficient of variation (CV)0.44342625
Kurtosis0.99387102
Mean29.361582
Median Absolute Deviation (MAD)6
Skewness0.51024466
Sum26161.17
Variance169.5125
MonotonicityNot monotonic
2026-01-22T16:59:35.568808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28202
22.7%
2430
 
3.4%
2227
 
3.0%
1826
 
2.9%
3025
 
2.8%
1925
 
2.8%
2124
 
2.7%
2523
 
2.6%
3622
 
2.5%
2920
 
2.2%
Other values (78)467
52.4%
ValueCountFrequency (%)
0.421
 
0.1%
0.671
 
0.1%
0.752
 
0.2%
0.832
 
0.2%
0.921
 
0.1%
17
0.8%
210
1.1%
36
0.7%
410
1.1%
54
 
0.4%
ValueCountFrequency (%)
801
 
0.1%
741
 
0.1%
712
0.2%
70.51
 
0.1%
702
0.2%
661
 
0.1%
653
0.3%
642
0.2%
632
0.2%
624
0.4%

SibSp
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52300786
Minimum0
Maximum8
Zeros608
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-22T16:59:35.757067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1027434
Coefficient of variation (CV)2.1084644
Kurtosis17.88042
Mean0.52300786
Median Absolute Deviation (MAD)0
Skewness3.6953517
Sum466
Variance1.2160431
MonotonicityNot monotonic
2026-01-22T16:59:35.906703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0608
68.2%
1209
 
23.5%
228
 
3.1%
418
 
2.0%
316
 
1.8%
87
 
0.8%
55
 
0.6%
ValueCountFrequency (%)
0608
68.2%
1209
 
23.5%
228
 
3.1%
316
 
1.8%
418
 
2.0%
55
 
0.6%
87
 
0.8%
ValueCountFrequency (%)
87
 
0.8%
55
 
0.6%
418
 
2.0%
316
 
1.8%
228
 
3.1%
1209
 
23.5%
0608
68.2%

Parch
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38159371
Minimum0
Maximum6
Zeros678
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-22T16:59:36.000529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80605722
Coefficient of variation (CV)2.1123441
Kurtosis9.7781252
Mean0.38159371
Median Absolute Deviation (MAD)0
Skewness2.749117
Sum340
Variance0.64972824
MonotonicityNot monotonic
2026-01-22T16:59:36.107416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0678
76.1%
1118
 
13.2%
280
 
9.0%
55
 
0.6%
35
 
0.6%
44
 
0.4%
61
 
0.1%
ValueCountFrequency (%)
0678
76.1%
1118
 
13.2%
280
 
9.0%
35
 
0.6%
44
 
0.4%
55
 
0.6%
61
 
0.1%
ValueCountFrequency (%)
61
 
0.1%
55
 
0.6%
44
 
0.4%
35
 
0.6%
280
 
9.0%
1118
 
13.2%
0678
76.1%

Ticket
Text

Distinct681
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2026-01-22T16:59:36.497938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.7508418
Min length3

Characters and Unicode

Total characters6015
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique547 ?
Unique (%)61.4%

Sample

1st rowA/5 21171
2nd rowPC 17599
3rd rowSTON/O2. 3101282
4th row113803
5th row373450
ValueCountFrequency (%)
pc60
 
5.3%
c.a27
 
2.4%
a/517
 
1.5%
ca14
 
1.2%
212
 
1.1%
ston/o12
 
1.1%
sc/paris9
 
0.8%
w./c9
 
0.8%
soton/o.q8
 
0.7%
soton/oq7
 
0.6%
Other values (709)955
84.5%
2026-01-22T16:59:37.050835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3746
12.4%
1689
11.5%
2594
9.9%
7490
8.1%
4464
 
7.7%
6422
 
7.0%
0406
 
6.7%
5387
 
6.4%
9328
 
5.5%
8282
 
4.7%
Other values (25)1207
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)6015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3746
12.4%
1689
11.5%
2594
9.9%
7490
8.1%
4464
 
7.7%
6422
 
7.0%
0406
 
6.7%
5387
 
6.4%
9328
 
5.5%
8282
 
4.7%
Other values (25)1207
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3746
12.4%
1689
11.5%
2594
9.9%
7490
8.1%
4464
 
7.7%
6422
 
7.0%
0406
 
6.7%
5387
 
6.4%
9328
 
5.5%
8282
 
4.7%
Other values (25)1207
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3746
12.4%
1689
11.5%
2594
9.9%
7490
8.1%
4464
 
7.7%
6422
 
7.0%
0406
 
6.7%
5387
 
6.4%
9328
 
5.5%
8282
 
4.7%
Other values (25)1207
20.1%

Fare
Real number (ℝ)

High correlation  Zeros 

Distinct248
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.204208
Minimum0
Maximum512.3292
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-22T16:59:37.507100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q17.9104
median14.4542
Q331
95-th percentile112.07915
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.0896

Descriptive statistics

Standard deviation49.693429
Coefficient of variation (CV)1.5430725
Kurtosis33.398141
Mean32.204208
Median Absolute Deviation (MAD)6.9042
Skewness4.7873165
Sum28693.949
Variance2469.4368
MonotonicityNot monotonic
2026-01-22T16:59:37.663042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0543
 
4.8%
1342
 
4.7%
7.895838
 
4.3%
7.7534
 
3.8%
2631
 
3.5%
10.524
 
2.7%
7.92518
 
2.0%
7.77516
 
1.8%
7.229215
 
1.7%
26.5515
 
1.7%
Other values (238)615
69.0%
ValueCountFrequency (%)
015
1.7%
4.01251
 
0.1%
51
 
0.1%
6.23751
 
0.1%
6.43751
 
0.1%
6.451
 
0.1%
6.49582
 
0.2%
6.752
 
0.2%
6.85831
 
0.1%
6.951
 
0.1%
ValueCountFrequency (%)
512.32923
0.3%
2634
0.4%
262.3752
0.2%
247.52082
0.2%
227.5254
0.4%
221.77921
 
0.1%
211.51
 
0.1%
211.33753
0.3%
164.86672
0.2%
153.46253
0.3%

Embarked
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
S
646 
C
168 
Q
77 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S646
72.5%
C168
 
18.9%
Q77
 
8.6%

Length

2026-01-22T16:59:37.806634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T16:59:37.888550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s646
72.5%
c168
 
18.9%
q77
 
8.6%

Most occurring characters

ValueCountFrequency (%)
S646
72.5%
C168
 
18.9%
Q77
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S646
72.5%
C168
 
18.9%
Q77
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S646
72.5%
C168
 
18.9%
Q77
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S646
72.5%
C168
 
18.9%
Q77
 
8.6%

Has_Cabin
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
0
687 
1
204 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0687
77.1%
1204
 
22.9%

Length

2026-01-22T16:59:37.996937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T16:59:38.075110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0687
77.1%
1204
 
22.9%

Most occurring characters

ValueCountFrequency (%)
0687
77.1%
1204
 
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0687
77.1%
1204
 
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0687
77.1%
1204
 
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0687
77.1%
1204
 
22.9%

FamilySize
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9046016
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2026-01-22T16:59:38.167252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6134585
Coefficient of variation (CV)0.84713704
Kurtosis9.159666
Mean1.9046016
Median Absolute Deviation (MAD)0
Skewness2.7274415
Sum1697
Variance2.6032485
MonotonicityNot monotonic
2026-01-22T16:59:38.278780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1537
60.3%
2161
 
18.1%
3102
 
11.4%
429
 
3.3%
622
 
2.5%
515
 
1.7%
712
 
1.3%
117
 
0.8%
86
 
0.7%
ValueCountFrequency (%)
1537
60.3%
2161
 
18.1%
3102
 
11.4%
429
 
3.3%
515
 
1.7%
622
 
2.5%
712
 
1.3%
86
 
0.7%
117
 
0.8%
ValueCountFrequency (%)
117
 
0.8%
86
 
0.7%
712
 
1.3%
622
 
2.5%
515
 
1.7%
429
 
3.3%
3102
 
11.4%
2161
 
18.1%
1537
60.3%

IsAlone
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
1
537 
0
354 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1537
60.3%
0354
39.7%

Length

2026-01-22T16:59:38.402153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T16:59:38.481204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1537
60.3%
0354
39.7%

Most occurring characters

ValueCountFrequency (%)
1537
60.3%
0354
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1537
60.3%
0354
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1537
60.3%
0354
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1537
60.3%
0354
39.7%

Title
Categorical

High correlation 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Mr
517 
Miss
185 
Mrs
126 
Master
 
40
Rare
 
23

Length

Max length6
Median length2
Mean length2.7878788
Min length2

Characters and Unicode

Total characters2484
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMr
2nd rowMrs
3rd rowMiss
4th rowMrs
5th rowMr

Common Values

ValueCountFrequency (%)
Mr517
58.0%
Miss185
 
20.8%
Mrs126
 
14.1%
Master40
 
4.5%
Rare23
 
2.6%

Length

2026-01-22T16:59:38.579179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-22T16:59:38.689986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mr517
58.0%
miss185
 
20.8%
mrs126
 
14.1%
master40
 
4.5%
rare23
 
2.6%

Most occurring characters

ValueCountFrequency (%)
M868
34.9%
r706
28.4%
s536
21.6%
i185
 
7.4%
a63
 
2.5%
e63
 
2.5%
t40
 
1.6%
R23
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M868
34.9%
r706
28.4%
s536
21.6%
i185
 
7.4%
a63
 
2.5%
e63
 
2.5%
t40
 
1.6%
R23
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M868
34.9%
r706
28.4%
s536
21.6%
i185
 
7.4%
a63
 
2.5%
e63
 
2.5%
t40
 
1.6%
R23
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M868
34.9%
r706
28.4%
s536
21.6%
i185
 
7.4%
a63
 
2.5%
e63
 
2.5%
t40
 
1.6%
R23
 
0.9%

ticket_type
Categorical

High correlation  Imbalance 

Distinct17
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
NUMERIC_TICKET
661 
PC
 
60
Rare
 
36
C.A.
 
27
A/
 
23
Other values (12)
84 

Length

Max length14
Median length14
Mean length11.391695
Min length1

Characters and Unicode

Total characters10150
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA/
2nd rowPC
3rd rowSTON/O
4th rowNUMERIC_TICKET
5th rowNUMERIC_TICKET

Common Values

ValueCountFrequency (%)
NUMERIC_TICKET661
74.2%
PC60
 
6.7%
Rare36
 
4.0%
C.A.27
 
3.0%
A/23
 
2.6%
STON/O18
 
2.0%
W./C.9
 
1.0%
SOTON/O.Q.8
 
0.9%
CA.8
 
0.9%
SOTON/OQ7
 
0.8%
Other values (7)34
 
3.8%

Length

2026-01-22T16:59:38.815758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
numeric_ticket661
74.2%
pc60
 
6.7%
rare36
 
4.0%
c.a27
 
3.0%
a23
 
2.6%
ston/o18
 
2.0%
ca14
 
1.6%
w./c9
 
1.0%
sc/paris9
 
1.0%
soton/o.q8
 
0.9%
Other values (5)26
 
2.9%

Most occurring characters

ValueCountFrequency (%)
C1461
14.4%
T1355
13.3%
I1331
13.1%
E1326
13.1%
R702
6.9%
N698
6.9%
M661
6.5%
U661
6.5%
_661
6.5%
K661
6.5%
Other values (15)633
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C1461
14.4%
T1355
13.3%
I1331
13.1%
E1326
13.1%
R702
6.9%
N698
6.9%
M661
6.5%
U661
6.5%
_661
6.5%
K661
6.5%
Other values (15)633
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C1461
14.4%
T1355
13.3%
I1331
13.1%
E1326
13.1%
R702
6.9%
N698
6.9%
M661
6.5%
U661
6.5%
_661
6.5%
K661
6.5%
Other values (15)633
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C1461
14.4%
T1355
13.3%
I1331
13.1%
E1326
13.1%
R702
6.9%
N698
6.9%
M661
6.5%
U661
6.5%
_661
6.5%
K661
6.5%
Other values (15)633
6.2%

Interactions

2026-01-22T16:59:31.310716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:25.600222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:26.365695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:27.217095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:28.873359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:29.655918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:31.420169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:25.720476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:26.518962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:27.668632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:28.994024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:29.781138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:31.548167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:25.860379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:26.658494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:28.048168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:29.128902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:30.528821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:31.687998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:25.983545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:26.793608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:28.359676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:29.259486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:30.866183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:31.805995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:26.109952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:26.936381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:28.625133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:29.382453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:31.066282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:31.918646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:26.239278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:27.071666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:28.746875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:29.511921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-22T16:59:31.188634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-22T16:59:38.921206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeEmbarkedFamilySizeFareHas_CabinIsAloneParchPassengerIdPclassSexSibSpSurvivedTitleticket_type
Age1.0000.151-0.1830.1260.2780.348-0.2170.0350.2650.106-0.1450.1580.3650.084
Embarked0.1511.0000.0830.1950.2260.1100.0520.0000.2580.1110.0920.1640.1300.332
FamilySize-0.1830.0831.0000.5290.0700.6420.801-0.0500.1370.2050.8490.2150.2520.543
Fare0.1260.1950.5291.0000.5820.3040.410-0.0140.4790.1890.4470.2830.0970.204
Has_Cabin0.2780.2260.0700.5821.0000.1520.0910.0630.7900.1340.1380.3130.1580.321
IsAlone0.3480.1100.6420.3040.1521.0000.6860.0000.1270.3000.8370.1980.4950.183
Parch-0.2170.0520.8010.4100.0910.6861.0000.0010.0220.2470.4500.1570.2690.244
PassengerId0.0350.000-0.050-0.0140.0630.0000.0011.0000.0320.066-0.0610.1040.0400.000
Pclass0.2650.2580.1370.4790.7900.1270.0220.0321.0000.1300.1480.3370.1890.447
Sex0.1060.1110.2050.1890.1340.3000.2470.0660.1301.0000.2060.5400.9920.146
SibSp-0.1450.0920.8490.4470.1380.8370.450-0.0610.1480.2061.0000.1870.2940.537
Survived0.1580.1640.2150.2830.3130.1980.1570.1040.3370.5400.1871.0000.5650.192
Title0.3650.1300.2520.0970.1580.4950.2690.0400.1890.9920.2940.5651.0000.105
ticket_type0.0840.3320.5430.2040.3210.1830.2440.0000.4470.1460.5370.1920.1051.000

Missing values

2026-01-22T16:59:32.108652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-22T16:59:32.296768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarkedHas_CabinFamilySizeIsAloneTitleticket_type
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500S020MrA/
1211Cumings, Mrs. John Bradley (Florence Briggs Thayer)female38.010PC 1759971.2833C120MrsPC
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250S011MissSTON/O
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000S120MrsNUMERIC_TICKET
4503Allen, Mr. William Henrymale35.0003734508.0500S011MrNUMERIC_TICKET
5603Moran, Mr. Jamesmale28.0003308778.4583Q011MrNUMERIC_TICKET
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625S111MrNUMERIC_TICKET
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750S050MasterNUMERIC_TICKET
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333S030MrsNUMERIC_TICKET
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708C020MrsNUMERIC_TICKET
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarkedHas_CabinFamilySizeIsAloneTitleticket_type
88188203Markun, Mr. Johannmale33.0003492577.8958S011MrNUMERIC_TICKET
88288303Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167S011MissNUMERIC_TICKET
88388402Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000S011MrRare
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500S011MrSOTON/OQ
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250Q060MrsNUMERIC_TICKET
88688702Montvila, Rev. Juozasmale27.00021153613.0000S011RareNUMERIC_TICKET
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000S111MissNUMERIC_TICKET
88888903Johnston, Miss. Catherine Helen "Carrie"female28.012W./C. 660723.4500S040MissW./C.
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C111MrNUMERIC_TICKET
89089103Dooley, Mr. Patrickmale32.0003703767.7500Q011MrNUMERIC_TICKET